Conference Agenda

Three-dimensional (3-D) radiative transfer effects of clouds on trace gases and aerosols have been studied extensively using satellite products and model simulations. In the vicinity of clouds, satellite measured reflectances are higher than the cloud-free scenes at the bright side of clouds and lower in the shadows. In order to understand the 3-D effects of clouds, we have developed a 3-D Monte Carlo radiative transfer model at KNMI (called MONKI). MONKI has been used to simulate TROPOMI measurements at UV wavelengths with polarization.

TROPOMI is a satellite spectrometer with a spatial resolution of 3.5 km x 5.5 km. The objective of TROPOMI is to provide accurate atmospheric composition products. We have used MONKI to simulate the TROPOMI NO₂ airmass factors and reflectances at 340 and 380 nm at different cloudy scenes. Various cloud optical thickness, cloud heights, and surface albedo values are specified in the simulations. Then Absorbing Aerosol Index (AAI) values are calculated for the simulated scenes using TROPOMI AAI algorithm. Based on the AAI features in the simulated scenes, we re-analysed the AAI data in the TROPOMI product in the shadows. For the NO₂ products, we simulated the NO₂ airmass factors using MONKI and compared with NO₂ airmass factor calculated using 1-D model simulations. Finally we analysed the TROPOMI NO₂ products in the shadowed pixels and in the cloud-free, shadow-free pixels to quantify the impacts of shadows on the NO₂ product.

Shadows from clouds and buildings present in high spatial resolution satellite imagery are typically filtered out in image processing. However, the shadows can be used to retrieve aerosol and surface properties simultaneously. In a new retrieval algorithm, the aerosol optical thickness is retrieved using the contrast between shadowed pixels and bright pixels and compared with AERONET data.

In the presentation we will report the progresses on the 3-D model simulations of AAI, NO₂ AMFs, impacts of shadows on NO₂ products, and the aerosol retrievals using shadows.

Northern Ireland's contribution to the UK's fifth carbon budget mandates a reduction in emissions of at least 35% by 2030 compared to the 1990 level. In comparison to the rest of the United Kingdom, Northern Ireland has relatively high percentages per capita emission in the agricultural, transportation, residential, LULUCF (land use, land use change, and forestry) and power sector. The electricity generated by the renewable energy is increasing since 2003 significantly. The increasing rate is nearly three times for the N. Ireland than the UK. In the year 2021, the electricity generated by the wind has increased to 47% (Figure 1). In this project we have conducted investigations into the current status of carbon emission in Northern Ireland (NI) along with the electricity generation situation through the renewable energy like wind and solar energy applications. Further more the types of renewable energy sources have been analysed. As comparison, the CO2 emission distribution in the NI has been observed by the GHGSat and a program has been developed to carry on analysis with the CO2 emission data collected during the past ten years. This developed tool will help us to study the effect of using the renewable energy for the power generation with the CO2 distribution in the atmosphere in the N.Ireland. It is also, the analysis will help us to understand the influence of different types of renewable energy to the CO2 reduction.

Figure 1 shows the total electricity energy consumption in N. Ireland along with the increased portion of electricity generated by the renewable energy since 2008. The electricity consumption in the past 15 year is reducing while the percentage of the electricity generated by the renewable energy is continuing increased from 6% in 2008 to 47% in 2022.

The Figure 2 shows the renewable energy applications since 1990 to 2022 with capacity up to 48MW. The installation of the renewable energy sites is continuing over time. It is aiming to find out the effect of CO2 reduction with the geo-distribution of the renewable applications.

Figure 3 shows the mirror image of the CO2 distribution in the N. Ireland in the past ten years.

Within the joint Sino-European project “Remote Sensing of Changing Coastal Marine Environments” (ReSCCoME) we are developing techniques for the use of Synthetic Aperture Radar (SAR) data for the monitoring of European and Chinese coastal areas. We demonstrate that a classification of sediments on exposed intertidal flats is possible, when complex SAR data acquired at different radar bands is used. Single-band SAR data can already be used to generate Digital Elevation Maps (DEM) through an identification of waterlines at different water levels. Here, two approaches, including a new neural network, are used to are yielding promising results. We further demonstrate that SAR wind fields yield a useful and robust tool to assess the potential of possible future wind farms, and to demonstrate the impact of existing windfarms on their surrounding environment, particularly the deficit in local wind speed.

The project aims at exploiting microwave satellite measurements to generate innovative added-value products to observe coastal areas characterized by harsh environments, even under extreme weather conditions. The following added-values products are addressed: water pollution, intertidal area monitoring, ship and metallic target observation, NN methods to retrieve wind direction from SAR imagery.

The following activities have been addressed:

Water pollution

Previous activities: Theoretical scattering models (under monostatic and bistatic configurations) have been developed to predict sea surface scattering with or without surfactants. In the monostatic case, theoretical predictions have been contrasted with actual measurements collected by the Synthetic Aperture Radar.

New activities: A model has been developed to shed light in the prediction of oil-sea contrast using different combinations of scattering (AIEM and two scale BPM) and damping (Marangoni and MLB) models.

Target detection

Previous activities: Multi-polarization backscattering from a known ship observed at different incidence angles. The analysis is carried on using metrics based on both power and phase information.

New activities: A new metric is defined, namely the polarization signature of the degree of polarization, that can be used to better asses the scattering variability at the variance of incidence angle for both sea and targets.

The polSAR backscatter from PAZ imagery acquired over the Robin Riggs wind farm is analysed to estimate blade rotation using sub-aperture analysis

Intertidal area monitoring

New activities: A data set that consists of X-band (CosmoSkyMed and PAZ), L-band (ALOS-2) and C-band (RadarSAT-2 and Sentinel-1) polarimetric SAR scenes has been acquired in the Scottish Solway Firth intertidal area to discuss the variability of the polarimetric scattering against SAR frequency and incidence angle over a common area.

Wind speed

Previous activities: SAR and ancillary scatterometer and model-based information are used to estimate the wind vector from SAR scenes under moderate and extreme weather conditions.

New activities: A new processing chain that exploits NN to estimate wind direction from the SAR imagery is proposed and tested using X-band CosmoSkyMed SAR imagery augmented with ancillary ASCAT and ECMWF info.

All this matter will be detailed in the proposed piece of study.

Sea ice is a highly sensitive indicator of past and present climate change. The demand for getting comprehensive, continuous, and reliable sea ice information from multi-source satellite data is growing as a result of climate change and its impact on environment and regional weather conditions, and on human activities such as operations in ice-covered ocean regions. This paper provides an overview of the Dragon 5 project dealing with synergistic monitoring of sea ice in the Arctic by multi-source remote sensing data.

For sea ice classification, the multi-frequency polarimetric backscatter behavior of sea ice during the melt period was investigated. Multi-frequency (L-, S-, C-, X- and Ku-band) airborne SAR scenes were recorded in the Bohai Sea with air temperatures varying around 0℃. In this work, we quantified the redundancy and relevance of polarimetric features for identifying ice types during the melting period, and assess the discrimination ability of melting sea ice types at the different radar frequencies. Considering the needs of operational Ice Services responsible for producing sea ice maps, another study dealt with a comparison of ice type separation in satellite C- and L-band SAR images as stand-alone and in combination. Since L- and C-band SAR systems have to be operated from different satellite platforms, an optimal data acquisition strategy has also to be developed. For sea ice thickness, we analyzed the feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter. To this end, we used the HY-2B radar altimeter to retrieve the Arctic radar freeboard and sea ice thickness, and compared the results with the co-incident CryoSat-2 products by AWI. By comparing with the OIB and IceSAT-2 data, we found that the deviations in radar freeboard and sea ice thickness between HY-2B and CS-2 over multiyear ice are larger than those over first-year ice. For iceberg detection by SAR data, the variations of signature contrast between icebergs and sea ice dependent on ice conditions and radar parameters was investigated. We found that the intensity contrast depends on the radar frequency, the incidence angle and the sea ice surface characteristics. The latter study will be presented by our young investigators.

Sea ice drift and thickness retrieval methods that are specifically designed for the FY-3D radiometer were proposed. For sea ice drift in the Arctic we used a continuous maximum correlation (CMCC) approach. To address the challenge of retrieving Arctic sea ice thickness, a FY-3D specific method was developed that relies on different parameters derived from the brightness temperature data (i.e. polarization ratio and gradient ratio). Besides estimating sea ice thickness with radiometer data we also investigated detection of thin ice (<20 cm) in the Arctic using AMSR2 and FY-3C radiometer data. The thin ice detection is based on the classification of the 36 GHz polarization ratio and H-polarization 89-36 GHz gradient ratio (GR) with linear discrimination analysis, and thick ice restoration with GR3610H. An integral part of the thin ice detection is the atmospheric correction of the brightness temperature data, following an EUMETSAT OSI SAF correction scheme. The thin ice detection algorithm was developed using MODIS ice thickness charts over the Barents and Kara Seas. The AMSR2 and FY-3C daily thin ice charts are calculated for one winter season, and their statistical similarities and differences are investigated. They are also compared against the SMOS ice thickness data. The AMSR2 and MWRI daily thin ice charts are targeted to be used together with SAR imagery for sea ice classification.

The contribution of meltwater from the snowpack and glaciers in High Mountain Asia (HMA) is rather well documented, as are changes in glacier extent and volume. Less explored are the overall dynamics of the high mountain water cycle, and the interactions of snow and ice dynamics with those of vegetation to shape HMA catchments response to weather and climate and their water yield .
This is the goal of our project and we made progress on several aspects, linking progresses in remote sensing and advanced land surface modelling to advance simulations of HMA water cycle. Inter- and intra-annual elevation changes of glaciers in the HMA region in 2003–2020 were studied using Ice, Cloud and land Elevation Satellite (ICESat) data and Shuttle Radar Terrain Mission (SRTM) digital elevation model (DEM) data. The inter-annual change of glacier elevation in 2003–2020 had large spatial heterogeneity. Glacier elevation reduction mainly occurred in the marginal region of the HMA with the maximum decline in the Nyainqentanglha region, while glacier elevation increased in the West Kunlun of inner HMA regions in 2003–2020. The intra-annual change of HMA glacier elevation in 2019 and 2020 showed a clear spatiotemporal heterogeneity, and the glacier thickening period was gradually delayed from the marginal area to the inner area of the HMA.
The inter- and interannual variability in snow cover during the period 2000 – 2020 was studied in the Tarim Basin to understand changes in glacier extent in relation with snow accumulation. We evaluated how observed trends in glacier area related to snow cover area in five subregions in the Tarim Basin. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. The analysis of the cross- correlation functions of glacier and snow cover areas showed that the glacier area responds to temperature, precipitation and snow cover within the same year.
Glacier surface albedo is one of the most important parameters to determine the net shortwave radiation and therefore affect glacier energy and mass balance, which in turn affect glacier surface flow, especially its spatial and temporal variability. The results show that the glacier albedo declined over the entire HMA, but with distinct spatial and seasonal differences. In the westerly-dominated regions, glacier albedo decreased slightly and even increased in the Hindu Kush and West Himalaya, while in the monsoon-dominated and transition regions, it showed a large decrease with the fastest change in the Inner Tibetan Plateau.
Patterns of glacier surface velocity and its seasonal and interannual variability in the temperate glaciers of the Parlung Zangbo Basin (PZB) are still uncertain. On the basis of satellite images acquired from 2013 to 2020, we produced a map of time-averaged glacier surface velocity and examined four typical glaciers (Yanong, Parlung No.4, Xueyougu, and Azha) in the PZB. Next, we explored the driving factors of surface velocity and of its variability. The results show that the glacier centerline velocity increased slightly in 2017–2020. The accumulated ice mass could have caused seasonal velocity changes in response to mass imbalance during 2017–2020. There was a clear winter-spring speedup of 40% in the upper glacier region, while a summer speedup occurred at the glacier tongue.
The generation of water in HMA headwaters and their downstream flow is not fully understood, as interactions in the mountain water cycle between the hydrosphere and biosphere remain elusive. Understanding how blue meltwater from snow-pack and glaciers contribute to total water vapour flux from vegetation, both is high elevation catchments and further downstream is a pressing and challenging research question. We applied a state-of-the-art land surface model (Tethys & Chloris) to the glacierized Langtang catchment in the Nepalese Himalayas to demonstrate the advantage of using high resolution earth observation data on e.g. glacier thinning and surface flow, glacier albedo and snow cover to constrain the meteorological uncertainty and validate our model results.

This research and collaboration project aims at the calibration and validation (Cal/Val) of the European Sentinel-3, Sentinel-6 and the Chinese HY-2B & HY-2C satellite altimeters using two permanent Cal/Val facilities: (1) the Permanent Facility for Altimetry Calibration established by ESA in Crete, Greece and (2) the National Altimetry Calibration Cooperation Plan of China. Other satellites, such as the Guanlan, CryoSat-2, CFOSAT, CRISTAL, etc., could certainly be supported by these Cal/Val infrastructures. Both facilities attain the strategy of Fiducial Reference Measurements (FRM), established by the European Space Agency for reporting calibration of satellite altimeters.

Calibration of satellite altimeters has been accomplished by examining actual satellite observations in open seas against reference ground measurements defined by Cal/Val infrastructures at specific locations in Europe and China.

During this third year of this Dragon-5 collaboration, the following tasks are being carried out:

• An exhaustive analysis of all sources of uncertainties (i.e., water level observations, satellite signal delays, reference surface models, etc.) which each makes a contribution to the final results of satellite altimeter calibrations;
• Reference measurements and all kind of observations on the ground at the Cal/Val infrastructures are connected to the International System of Units (SI) and absolute international standards of reference (speed of light, atomic time, etc.);
• Uncertainties for the results of satellite calibration are reported in a realistic, universal and objective way;
• Independent and diverse techniques for estimating altimeter biases are applied by the European and Chinese teams;
• Final Cal/Val results are reported and intercompared at the two Cal/Val facilities.

The main findings of this joint work are:

• Both European and Chinese Cal/Val sites are operational and implement independent calibrations for European, Chinese and international satellite altimeters (i.e., Sentinel-6A MF, Sentinel-3A/B, HY-2B/C, Jason-3, etc.);
• Operations and data processing are standardized at both Cal/Val reference infrastructures in Europe and China;
• Common procedures are implemented (i.e., tide gauge quality control, GNSS positioning, etc.) towards standardization and unification of operations and data handling and processing.
• The performance of satellite altimeters is continuously monitored by the two Cal/Val infrastructures in China and Europe.

The report we are presenting focuses on the objectives and schedule of our project, providing an update on the ongoing activities and results of Bufeng-1 data processing, calibration workflow, and validation of the calibrated results on hurricane winds, soil moisture, and sea level measurements. This presentation is divided into three parts. Firstly, we will provide a brief introduction about Bufeng-1 and recent Chinese GNSS-R missions, highlighting their significance in the field. Secondly, we will delve into the preliminary results obtained by utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), earth reflectivity, and range measurements. The preliminary results indicate that Bufeng-1 has a high agreement compared with other observations on severe sea surface winds, soil moisture, and sea level. We will align the measurements of Bufeng-1 with SFMR collected hurricanes, SMAP derived soil moisture, and DTU18 sea level models to provide a comprehensive analysis of the results. We will also analyze the accuracy and correlation coefficients to discuss the limitations and issues for future research. This will be crucial in improving the quality of data and enhancing the accuracy of future measurements. For the last part, we will give the outlook about our future works of the objectives and the future plan of Chinese GNSS-R missions. Our aim is to provide a comprehensive and detailed report that will assist researchers and stakeholders in the field of climate research, weather forecasting, and disaster management in making informed decisions.

The pace of urbanization has been unprecedented. Rapid urbanization poses significant social and environmental challenges, including sprawling informal settlements, increased pollution and urban heat island effects, loss of biodiversity and ecosystem services, and increased vulnerability to disasters. Therefore, timely and accurate information on urban change patterns is crucial to support sustainable and resilient urban planning and monitoring of the UN 2030 Urban Sustainable Development Goal (SDG). The overall objective of this project is to develop innovative, robust and globally applicable methods, based on Earth observation (EO) big data and AI, for urban land cover mapping and urbanization monitoring.

Using ESA Sentinel-1 SAR, Sentinel-2 MSI and Chinese GaoFen-1 images, the EO-AI4Urban team has developed varous deep learning-based methods for urban mapping and change detection. For urban mapping, a novel Domain Adaptation (DA) approach using semi-supervised learning has been developed for urban extraction. The DA approach jointly exploits Sentinel-1 SAR and Sentinel-2 MSI data to improve across-region generalization for built-up area mapping [1]. For urban change detection, several novel methods have been developed including a dual-stream U-Net [2] and a Siamese Difference Dual-Task network with Multi-Modal Consistency Regularization [3]. Further, a high-resolution feature difference attention network (HDANet) is proposed to detect changes using the Siamese network structure [4]. Another novel procedure was designed to search for built-up changing patterns with the joint use of temporal and spatial properties, starting from high-frequency SAR time series. The methodology has been tested on the city of Wuhan and considering a SAR series from March 2018 to March 2021 [5] [6]. Additionally, a novel automatic deep learning-based binary scene-level change detection method that trains a Scene Change Detection Triplet Network (SCDTN) using the automatically selected scene-level training samples was proposed [8]. A machine learning method was also developed using Landsat time series, to map built-up areas and to analyze changes during 2000 to 2020 [9]. Finally, to identify similar urban areas quickly and to reduce the cost of manually labeled data, a multisource data reconstruction-based deep unsupervised hashing method was proposed for unisource remote sensing image retrieval, called MrHash, which consists of a label generation network and a deep hashing network [9].

Experiments conducted on a test set comprised of sixty representative sites across the world showed that the proposed DA approach achieves strong improvements upon fully supervised learning. The fusion DA offers great potential to be adapted to produce easily updateable human settlements maps at a global scale [1]. Using the OSCD dataset, the results showed that the dual-stream U-Net outperformed other U-Net-based approaches together with SAR or optical data and feature level fusion of SAR and optical data [2]. Using bi-temporal SAR and MSI image pairs as input, the Siamese Difference Dual-Task network with Multi-Modal Consistency Regularization have been tested in the 60 sites of the SpaceNet7 dataset. The method achieved higher F1 score than that of several supervised models when applied to the sites located outside of the source domain [3]. Using several public building change detection datasets, the experimental results showed that the HDANet can achieve a high building change detection accuracy, compared with the current mainstream methods, with public building change detection datasets [4]. Using Landsat time series, the results show that machine learning method could extract built-up areas effectively. To analyze urbanization in 13 cities in the Beijing–Tianjin–Hebei region, SDG indicator 11.3.1, the ratio of land consumption rate to population growth rate (LCRPGR) is calculated and the results show that the LCRPGR in Beijing–Tianjin–Hebei region fluctuated significantly. Apart from the megacities of Beijing and Tianjin, after 2010, the LCRPGR values were greater than 2 in all the cities in the region, indicating inefficient urban land use [7]. The results for the scene-level changes between the bi-temporal VHR images showed that the proposed SCDTN method achieved the highest F1 score of 81.85% [8]. Conducting experiments on Sentinel-2 and GF-1 satellite images, the results showed that MrHash yielded the best performance among all methods [9].

References:

[1] Hafner, S., Y. Ban and A. Nascetti, 2022a. Unsupervised Domain Adaptation for Global Urban Extraction Using Sentinel-1 and Sentinel-2 Data. Remote Sensing of Environment. Volume 280, 113192.

[2] Hafner, S., A. Nascetti, H. Azizpour and Y. Ban, 2022b. Sentinel-1 and Sentinel- 2 Data Fusion for Urban Change Detection Using a Dual Stream U-Net. IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5.

[3] Hafner, S., Y. Ban and A. Nascetti, 2023. Multi-Modal Consistency Regular- ization Using Sentinel-1/2 Data for Urban Change Detection. International Journal of Applied Earth Observation and Geoinformation (under review).

[4] Wang, X., J. Du, K. Tan, J. Ding, Z. Liu, C. Pan, and B. Han, 2022. A high-resolution feature difference attention network for the application of building change detection, International Journal of Applied Earth Observation and Geoinformation, Volume 112, 102950.

[5] M. Che, A. Vizziello and P. Gamba, 2022. Spatio-temporal Urban Change Mapping with Time-Series SAR data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6] Che, M., A. Vizziello, P. Gamba. 2021. Spatio-temporal Change Mapping with Coherence Time-Series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7] Zhou, M., Lu, L., Guo, H., Weng, Q., Cao, S., Zhang, S., & Li, Q. (2021). Urban Sprawl and Changes in Land-Use Efficiency in the Beijing–Tianjin–Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data. Remote Sensing, 13(15).

[8] H. Fang, S. Guo, X. Wang, S. Liu, C. Lin and P. Du. 2023. Automatic Urban Scene-Level Binary Change Detection Based on a Novel Sample Selection Approach and Advanced Triplet Neural Network, IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18.

[9] Y. Sun, Y. Ye, J. Kang, R. Fernandez-Beltran, Y. Ban, X. Li, B. Zhang, and A. Plaza. 2022 Multisource Data Reconstruction-based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16.

Presenting Authors: Gao, Mingliang and Cartalis, Constantinos
-

The scope of the work is to demonstrate the potential of Earth Observation to support urban security. The work is deployed in a two-fold manner.

At a first stage, the evolution of groundwater flow field and the corresponding response of land subsidence along Yongding River (Beijing section) were analyzed by performing spatio-temporal analysis, time series decomposition, based on the data sets covering traditional hydrogeological data, groundwater observation data, and satellite-based images. Results showed that, at present, ecological water replenishment of Yondding River has no obvious impact on the formation deformation, but the rising groundwater level and differential land subsidence in some regions will pose a great risk to the safety of coastal areas in the future. In addition, the Beijing section of the Yongding River crosses multiple subway lines, and the affected area is close to the Beijing Daxing International Airport. Local groundwater level rising may cause underground facilities damage, and uneven land subsidence may cause surface & underground structure break, as well as the stability of electronic equipment, which affect the safe operation of airports and rail transit.

At a second stage, the dynamics of urban heat risk were analyzed by means of a tool that is based on the use of high-resolution Earth Observation (EO), climate, and socioeconomic data and exploits the potential of machine learning. The tool is developed in the cloud-based Google Earth Engine (GEE) platform that effectively addresses the challenges of big data analysis in studying urban heat risk. Urban heat risk maps are created for Beijing and Athens using clustering algorithms, which group areas with similar characteristics and assign them to different heat risk categories based on the spatiotemporal patterns of the above-mentioned indicators. The results effectively identify vulnerable regions that experience significantly higher heat risk and constitute intracity thermal heat spots. To this end, scientific evidence may be used in support of spatially differentiated resilience plans for climate extremes at the city scale.

Recommendations on the use of Earth Observation for urban security will be provided along with a discussion on other urban challenges that may be addressed accordingly.

Using the soil evapotranspiration model based on the improved Priestley Taylor (PT) method, combined with fAPAR data and surface reflectance (albedo) data from the MODIS, monthly soil evapotranspiration with 5 km spatial resolution in typical agricultural areas in China and Europe from 2017 to 2021 was estimated. Moreover, the analysis of the spatial and temporal variation characteristics of evapotranspiration were carried out. The model result was validated using field data obtained from farming observation stations in North China, and RMSE and R² were calculated. It showed that the soil evapotranspiration estimation model achieved good results in the farmland area, where the RMSE was around 1 mm and R² was around 0.8, indicating that the model can well simulate the spatial and temporal variation of soil evapotranspiration in the farmland and is suitable for further analysis of key evapotranspiration variables on spatial and temporal bases covering typical farming areas.

The easy access to the high-resolution satellite data at 10-to-30-meter resolution makes the agricultural remote sensing technology develop even faster. Under the support of the Dragon program, the sentinel series satellite in Europe and GF series satellite in China are providing the data options for agricultural monitoring as well as enhancing the capability of agricultural monitoring in general. Because of the diversified cultivation patterns in China, there are existing the big fields with one crop type and the small fields with the mosaic of various crop types. This fact is limited the application of satellite data in agricultural monitoring in China, therefore, the users have to make a compromise between the spatial resolution and the size of study area. In general, it had better use even higher resolution satellite image for crop monitoring in order to adapt to the crop cultivation situation in China. This project has made the great progress since the inception of this project. Two types of study areas were selected. The first one is with big fields and good at the development of modern agriculture that is comparable with the European agricultural farms. Another one is the typic northern Chine fields with the conventional agricultural development that is challenging for the agricultural monitoring with remote sensing data. The crop types in the study areas are winter wheat, crop, rice, and vegetable, representing the irrigation agriculture and rain fed agriculture in northern China. The project used the Chinese and European satellite data and the third partner satellite data to retrieve the crop growth and crop management information at field level in order to provide timely information to improve agricultural management. Through this joint project and the heavy involvement of young scientists from Europe and China, the satellite data finely processing and information retrieval algorithm is being exchanged and it is expected to bring a step forwards to support agricultural monitoring at fine scale.

EMPAC addresses different aspects related to the air quality (AQ) over China: aerosols, trace gases and their interaction through different processes, including effects of radiation and meteorological, geographical and topographical influences. Satellite and ground-based remote sensing together with detailed in situ measurements provide complimentary information on the contributions from different sources and processes affecting AQ, with scales varying from the whole of China to local studies and from the surface to the top of the boundary layer and above. Different species contributing to air quality are studied, i.e. aerosols, in AQ studies often represented as PM2.5, trace gases such as NO2, NH3, Volatile Organic Compounds (VOCs) and O3. The primary source of information in these studies is the use of a variety of satellite-based instruments providing data on atmospheric composition using different techniques. However, satellite observations provide column-integrated quantities, rather than near-surface concentrations. The relation between column-integrated and near-surface quantities depends on various processes. This relationship and the implications for the application of satellite observations in AQ studies are the focus of the EMPAC project. Initial results of detailed process studies using ground/based in situ measurements, instrumented towers, as well as remote sensing using lidar and Max-DOAS will be presented. A unique source of information on the vertical variation of NO2, O3, PM2.5 and BC is obtained from the use of an instrumented drone.
The results of the third year will be presented, including trend studies of air pollutants, algorithm development for aerosol retrieval, and analyses of NOx emissions from Sentinel 5p over the YRD.

This project is focused on multi-orbits applications of SAR imaging, and is intended to support use of multi-pass data stacks from: the upcoming P-Band mission BIOMASS; future L-Band missions, such as the SAOCOM constellation, the upcoming Chinese L-Band bistatic Mission Lu-Tan1, and potentially Tandem-L and Rose-L; the C-Band Sentinel Missions.

The main results until now are summarized into 4 contributions:

1. A detailed experimental analysis was carried out to compare two techniques for estimating forest height and vertical structure using airborne synthetic aperture radar (SAR) data, namely SAR tomography (TomoSAR) and the phase histogram (PH) technique. Using multiple SAR images, TomoSAR allows for a direct imaging of the three-dimensional (3D) electromagnetic structure of the vegetation layer, from which biophysical parameters such as forest height and underlying terrain topography can be extracted. The PH technique assigns each pixel in a SAR interferogram to a specific height bin based on the value of the interferometric phase, allowing for a local estimation of the vertical profile of forest scattering by accumulation of pixels fall within a given spatial window.

Results indicate that the PH technique can only loosely approximate the vertical structure produced by SAR tomography, but it can be used to produce a fairly good estimate of forest height. In particular, using the datasets collected by the TomoSense campaign, TomoSAR and PH techniques are observed to produce an average root mean square error (RMSE) of 2.63 m and 4.72 m in NW flight data, and 1.86 m and 5.26m in SE flight data, respectively. The observed results are interpreted in light of a simple physical model to predict phase variations in the two cases where forest scattering is determined by the presence of a dominant scatterer at each resolution cell or by a multitude of elementary scatterers, leading to the conclusion that the PH technique is best fit for the case of high- or very high-resolution data at higher frequency bands. Overall, the analysis in this paper demonstrates, both theoretically and experimentally, that the PH technique cannot achieve the same performance as multi-baseline tomography when applied to lower frequency data at a resolution of few meters. Yet, even in these conditions we remark that the PH technique allows for the retrieval of forest height based on a single interferogram at a single polarization. This makes the PH technique extremely interesting in the context of spaceborne missions.

2. A solution is proposed to the problem of atmospheric estimation using SAR data and a network of GNSS stations on the ground. The raw data from each station is processed to extract a GNSS-derived APS. Then, the SAR-derived APS is extracted on the spatial location of the GNSS stations. Such measurements are the sum of the true APS and the orbital error. We use the GNSS-derived APS as a ground truth, removing them from the SAR-derived estimates leading to a set of measurements of the pure orbital error. An inverse problem is solved, leading to two parameters characterizing the orbital error. The benefit of this inversion is double. First of all, the two estimated parameters can be used to provide a quality proxy for the trajectory. Second, the two parameters can be used to compute the forward model on the whole grid of the APS map (and not just on the set of GNSS stations as done before), leading to a calibration phase screen.

The procedure is tested using a dataset of more than 30 Sentinel-1 images and a network of GNSS stations in Sweden. The algorithm shows excellent performance. The validation process compares a set of independent GNSS stations with the SAR-derived APS before and after the calibration procedure. A second validation is carried out using a separate NWPM showing, once again, very good performances.

3) An alternative approach is proposed for NESZ estimation that exploits an interferometric pair of images over land. The method is based on the relation between coherence and noise. we use the stack to generate a set of interferograms with short temporal baselines. Each interferogram is fitted with a coherence model, and all measures are averaged to improve robustness. By repeating for each incidence angle, the NESZ profile of the new satellite can be characterized. The procedure was validated and tested using a stack of Sentinel-1A (S1A) and Sentinel-1B (S1B) images. First, the noise level of S1B was obtained separately according to Equation (1). Then, S1B’s NESZ was estimated by the procedure described above, using the stack of S1A data. The comparison between the two results confirmed that the noise level of a new satellite could be characterized over land, with as little as one available product.

4) InSAR has been widely recognized as an effective tool for landslide investigation. However, its measurement accuracy is largely limited by the complex atmospheric delay distortion in alpine valley regions, resulting in poor performance of landslide detection and monitoring. Particularly, the spatial atmospheric heterogeneity over wide areas cannot be accurately reflected by conventional empirical phase-elevation models or external data-based methods. Here we proposed a multi-temporal moving-window linear model (MMLM) to correct the tropospheric delay for wide-area landslide investigation. This is a linear regression model based on the elevation-phase relationship for modeling multi-temporal phases within a sliding local window. It mitigates the influence of local turbulent phase, local landslide deformation, and phase unwrapping error on parameter estimation, providing precise heterogeneous InSAR atmospheric corrections for wide-area landslide identification and deformation monitoring. Experimental results with descending and ascending Sentinel-1 data over the reservoir area of the Lianghekou hydropower station clearly demonstrated that the proposed MMLM model outperforms modern APS correction approaches including the ERA5, GACOS, spatial-temporal filtering, and traditional linear model.

Our project aims to develop and validate innovative products for inland, transitional and coastal waters to support and improve the water ecosystem services, sustainable management and security. We have made some progress on the algorithms and applications of optical remote sensing images on oil spill detecting and water quality retrieving.

Firstly, we have made some progress on optical remote sensing image preprocessing. We developed an OWT (Optical Water Types) based method for flagging land-affected signal. The developed method improved the retrieval of water quality parameters. Results show a seasonality in the land-affected signal driven mainly by sun geometry and land cover. Besides, we tested different atmospheric correction models against in-situ hyperspectral data and evaluated their performance over coastal waters.

Secondly, we applied different kinds of satellite data to detect oil spills. We assessed the performance of Ultraviolet Imager (UVI) onboard Haiyang-1C/D (HY-1C/D) satellites by the following aspects: image features of oils under sunglint, sunglint requirement for spaceborne UV detection of oils, and the stability of the UVI signal. The results indicated that in UVI images, it is sunglint reflection that determines the image features of spilled oils, and the appearance of sunglint can strengthen the contrast between oils and seawater. Besides, we proposed an object-based spectra comparison (OBSC) approach to extract emulsified oil slicks from Balikpapan Bay, Indonesia, using optical imagery from Sentinel-2 Multispectral Instrument (MSI) and PlanetScope. We used optical imagery from Landsat-8 OL to detect oil slicks on the ocean surface through spatial analysis and spectral diagnosis in the northern South China Sea (NSCS). We demonstrated the capability of medium-resolution optical imagery in monitoring regional oil spills.

Thirdly, we developed several algorithms for retrieving water quality parameters, including CDOM (Colored Dissolved Organic Matter), Chla (chlorophyll-a), and water clarity. We proposed a blended CDOM algorithm based on OWT classification. Results showed that the blended algorithm has higher accuracy in CDOM estimating than a single algorithm for all waters. We also proposed an optical classification algorithm to exclude highly turbid waters, and then to estimate Chla in the less turbid waters only. We constructed an exponential estimation model based on Rrs(NIR)/Rrs(red), and applied the model to Landsat TM and OLI images in Lake Taihu to analyze its Chla spatiotemporal distribution. We also proposed a modified model of the quasi-analytical algorithm to retrieve the water clarity of inland waters across Hainan Island, China using Sentinel-2 multispectral instrument data. Based upon this, the first spatiotemporal analysis of recent water clarity in Hainan Island was conducted.

Coastal regions are vital places for the economy, sustainability, and environmental care of entire nations with severe impacts on a global scale. However, coastal regions are vulnerable to natural disasters. The coastal regions are particularly exposed to extreme events and the effects of global climate change. Remote sensing (RS) technologies play a significant role for: i) monitoring disturbances of public/private infrastructures, ii) helping cultural/natural heritage preservation, iii) handling and maintain effective and updated disaster risk management plans, and iv) managing efficient agriculture processes. In this context, the joint European Space Agency (ESA) – Ministry of Science and Technology of China (MOST) DRAGON V GREENISH project was designed to develop and apply conventional and new algorithms for the detection and mapping of flooded areas, the analysis of urban climate-related threats and the anthropogenic disasters (e.g., ground subsidence in coastal areas and over reclaimed-land platforms), to improve the knowledge and develop innovative RS methods. GREENISH is the result of international cooperation between some European and Chinese research centers that operate in the remote sensing (RS) sector. The main project goals are: i) to detect and study the ground deformations in coastal/deltaic regions using conventional and novel interferometric synthetic aperture radar approaches; ii) to monitor changes through coherent and incoherent change detection analyses; iii) to study coastal erosion, using high-resolution optical and SAR images; iv) to assess sea level rise (SLR) and hydrogeological risks in urban coastal areas; v) to train Young Scientists (YS).

Within this framework, SAR remote sensing is a valuable tool for detecting and monitoring flood phenomena, allowing the differentiation between inundated and non-inundated areas. This work and the presentation planned at the next D5 symposium aim to summarizes the project's key achievements during the recent years and provide insights on the forthcoming activities. A special focus will be on the application/derivation of new RS techniques, also aided with artificial intelligence tools and methods. More specifically, starting from a sequence of calibrated, co-registered SAR acquisitions, the family of used methodologies for change detection analyses of Earth’s surface consists of different modules that span from the generation of proper change detection indices to the integration of these pieces of information with those achievable using novel interferometric SAR approaches, also aided by AI and multi-grid techniques. Moreover, methods about evaluation of regional disaster reduction risk capacity are also developed. Accessibility and location of emergency shelters in coastal mega city under extreme waterlogging disasters are also analyzed.

Part 1, Glacier velocity estimation based on Sentinel-2 observations at Karakoram.

The Sentinel-2A/B Twin satellites provide 5-day repeat observation to the Earth and capable of deriving glacier velocity with high-temporal resolution. In this study, the ‘Karakoram-Pamir anomaly’ region was taken as the study site and a data processing procedure was proposed to derive quasi-monthly glacier flow velocity fields. Each acquisition is performed offset-tracking to its next three almost cloud-free acquisitions to increase number of redundant observations. The detector mosaicking errors are eliminated if offset-tracking is performed between two different Sentinel-2 satellites. Flow speed and direction referenced method is taken to remove the wrong matching of offset-tracking. Then an iterative SVD method solves the glacier velocity and removes the observation with large residual. According to the glacier flow velocity time series between Oct 2017 and Sep 2021, it captures plenty of surged glaciers start and/or end their surging phases across this region. Two types of surging glaciers are identified according to the shape of their high temporal resolution flow rates time series. The first types’ surging phase last for only a few years, and shows no seasonal variation. Rimo’s southern tributary is an example of this type, it experienced a full surging phase during our study period and last for about two years, the maximum speed exceeded 10 m/day. Another type behaves similar to a normal type glacier but with glacier front advancing and much higher summer speed than their stagnation phase, such as Gando at Pamir. Normal type glaciers also presented annually speed up and slow down, with acceleration started usually in late April or earth May, and ends before September.

Part 2, Greenland ice sheet melting and re-freezing status monitoring with Sentinel-1 imagery

First this study introduced a method of incidence angle normalization to the backscatter coefficient of dual-polarized Sentinel-1 images. A multiple linear regression model is trained using the ratio between backscatter coefficient differences and incidence angle differences of quasi-simultaneously observed ascending and descending image pairs. Regression factors include geographical position and elevation. The precision evaluation of the ascending and descending images suggests better normalization results than the widely-used cosine-square correction method for HH images and little improvement for the HV images.

Referring to the 2m air temperature data of AWS, we find that the daily average 2m air temperature higher than 0℃ cannot accurately indicate if the ice sheet melted. The daily maximum 2m air temperature on two consecutive days higher than 0°C and the daily average 2m air temperature exceeds -1°C on the SAR acquisition day that recorded by the AWS find good agreements with the -3dB decrease of the backscatter coefficients. The overall agreement and Kappa coefficients are mostly better than 0.85 and 0.70, respectively. However, at the ablation zone, although backscatter coefficient drops when the melting begins, but it also increases during the melting status, resulting a lower estimation of the melting duration.

Part 3, Glacier velocity estimation based on both Sentinel-1 and -2 observation at Greenland Ice Sheet

Two different methods are designed for deriving glacier velocity fields for Greenland Ice Sheet. The first is designed for area where Sentinel-1 6 or 12 days interferogram show certain level coherence. To overcome the high gradient of phase, a method of re-differential interferometry that employs the result of offset-tracking is designed. The maximum capacity of detecting deformation is ~3.6m for 6-day interferogram than conventional D-InSAR. The second method is designed for quick flowing area, where no coherence can be found for the Sentinel-1 interferogram. This method introduces a small baseline offset-tracking to Sentinel-1 and -2 images, then a least square method based on connective component is applied. SAR images can hardly give acceptable result at wet-snow zone during the melting seasons, while optical images are not obtained from Oct to next Mar. Error propagation theory is employed for precision analysis. Then a weighted least square method based on connective component method combines the time series derived from Sentinel-1 and -2. This method can provide a full time series of glacier velocity fields. The errors of Sentinel-1 images offset-tracking are ~0.4 m while ~2.5 m for Sentinel-2.

Glaciers are crucial sources of freshwater in particular for the arid lowlands surrounding High Mountain Asia. In order to better constrain glacio-hydrological models, annual, or even better, seasonal information about glacier mass changes is highly beneficial. In this study, we test the suitability of very high-resolution Pleiades DEMs to measure glacier-wide mass balance at annual and seasonal scales in two regions of High Mountain Asia (Muztagh Ata in Eastern Pamir and parts of Western Nyainqêntanglha, South-central Tibetan Plateau), where recent estimates have shown contrasting glacier behavior. We find that the average annual mass balance in Muztagh Ata between 2020 and 2022 was -0.11 ±0.21 m w.e. a^-1, suggesting the continuation of a recent phase of slight mass loss following a prolonged period of balanced mass budgets previously observed. The mean annual mass balance in Western Nyainqêntanglha for the same period was highly negative (-0.60 ±0.15 m w.e. a^-1 on average), suggesting increased mass loss rates. The 2022 winter (+0.21 ±0.24 m w.e.) and summer (-0.31 ±0.15 m w.e.) mass budgets in Muztag Ata and Western Nyainqêntanglha (-0.04 ±0.27 m w.e. [winter]; -0.66 ±0.07 m w.e. [summer]) suggest winter and summer accumulation-type regimes, respectively. We support our findings by implementing a Sentinel-1–based Glacier Index to identify the firn and wet snow areas on glaciers and characterize accumulation type. The good match between the geodetic and Glacier Index results demonstrates the potential of very high-resolution Pleiades data to monitor mass balance at short time scales and improves our understanding of glacier accumulation regimes across High Mountain Asia.

This report provides an overview of the recent progress on the cross-calibration and validation of CSES/Swarm satellite magnetic field and plasma measurements.

(1)High precision magnetometer (HPM) has worked successfully more than 5 years to provide continuous magnetic field measurement since the launch of CSES. After rechecking these years data, it is necessary to make an improvement for fluxgate magnetometer (FGM) orthogonal calibration (to estimate offsets, scale values and non-othogonalities) and alignment (to estimate three Euler angles). The following efforts are made to achieve this goal: For orthogonal calibration, we further considered the FGM sensor temperature correction on offsets and scale values to remove the seasonal effect. Based on these results, Euler angles are estimated along with global geomagnetic field modeling and then the latitudinal effect for east component is improved. After considering above improvement, we can prolong the updating period of all calibration parameters from daily to 10 days, without the separation of dayside and nightside data. These algorithms will be helpful to improve HPM routine data processing efficiency and data quality to support more scientific studies.

(2)The first detailed analysis about the spacecraft potential (Vs) variations of Swarm satellites are provided, which are flying at about 400-500 km. Different to previous studies that investigate the extremely charging events, usually with spacecraft potential as negative as -100 V, we focus on the variation of Swarm Vs varying within a few negative volts. The Swarm observations show that the spacecraft at low Earth orbit (LEO) altitudes are charged slightly negative, varying between -7 V and 0 V, and with the majority around -2 V. Interestingly, a second peak of Vs is found at -5.5 V, though the event number is less by an order than the first peak around -2 V. The two groups show different spatial and temporal distributions. For the slighter negative charged group, at low and middle latitudes the Vs shows relatively larger values above the magnetic equator, and with much more negative values on the dayside at low and middle latitudes; at high latitudes, the Vs shows relatively negative values during local summer. For the deeper negative charged group, Vs at equatorial and low latitudes is slightly lower at the SAA region; and at high latitudes, the valid Vs observations appear mainly during local winter. We found for the first group much negative Vs is observed at regions with higher background plasma density, while for the second group much negative Vs is observed at regions with lower background plasma density.

(3) The high-resolution magnetic field measurements from ESA’s Swarm satellite constellation provide a good opportunity for revisiting the mean properties of ionospheric currents. Among the Swarm Level 2 data products, provided by ESA, are field-aligned current (FAC) estimates based on single-spacecraft (single-SC) and dual-spacecraft (dual-SC) solutions. For the more reliable dual-SC approach only magnetic signatures from currents flowing through the integration loop are considered. In the case of single-SC FAC estimates the magnetic effects of all remote current systems contribute also to the results. A direct comparison between the two FAC products at auroral latitudes reveals that the single-SC estimates systematically overestimate the current density of region 2 (R2) FACs (~15%) while underestimates the region 1 (R1) FACs (~10%). The differences between the two FAC products appear closely related to the location and direction of the horizontal polar electrojet (PEJ) at auroral latitudes. A direct comparison of these two current systems suggests an influence of the PEJ induced magnetic field on the solutions from single-SC FACs.

An IFS125HR has been deployed in Xianghe Integrated Observatory. Long-term operations were carried out for accumulating high quality data, which is significant for validating the satellite greenhouse gases products and for finding the signal of climate change. Methane (CH₄) is the second most important greenhouse gas after carbon dioxide. Accurate monitoring and understanding of its spatiotemporal distribution are crucial for effective mitigation strategies. In this study, the spatiotemporal variations of CH₄ over China were investigated based on the CH₄ products from 4 well-known satellites (GOSAT, TROPOMI, AIRS, and IASI). As we know, the spectrometers on board the 4 satellites are quite different in specifications as well as in the inversion algorithms. GOSAT and TROPOMI CH₄ retrieval use shortwave infrared spectra, having a better sensitivity near the surface, while IASI and AIRS CH₄ retrieval use thermal infrared spectra, showing a good sensitivity in the mid-upper troposphere. Therefore, GOSAT and TROPOMI observed higher CH₄ concentrations in the east and south, and lower concentrations in the west and north, which is highly related to the CH₄ emissions. IASI and AIRS show a more uniform CH₄ distribution over China, which is dominated by the variation of CH₄ at a high altitude. However, a large discrepancy is found among these satellite data. AIRS CH₄ mole fraction is systematically lower than the other 3 satellites. Significant differences in seasonal variations of CH₄ are observed between IASI and AIRS across several regions in China. The highest concentration of CH₄ was observed by AIRS in Inner Mongolia, which is probably due to the dust inferences above the bare soil.

Keywords: CH₄, spatiotemporal variation, methane measuring satellites, intercomparison, FTIR

The Earth is facing unprecedented climatic, geomorphologic, environmental and anthropogenic changes, which require global scale observation and monitoring. The interest is in understanding involving Earth Observations (EO) of large extended areas, and long periods of time, with a broad variety of satellite sensors. The collected EO data volumes are thus increasing immensely with a rate of many Terabytes of data a day. With the current EO technologies these figure will be soon amplified, the horizons are beyond Zettabytes of data.

1）“Pre-trained and fine-tuning" is one of main paradigms that pre-train a fundamental model with large-scale unlabelled data in a unsupervised learning way and then retrain it with a small amount of labeled data for downstream tasks. Pre-trained models are demonstrated to be of strong generalization and adaptation to multi-tasks. To address the challenges of the difficulty and high cost of manual ground truth labeling, a three-dimensional masked autoencoder (MAE) self-supervised learning method is designed based on an improved masked autoencoder (MAE) self-supervised framework for SAR and optical image joint self-supervised learning to enhance the feature extraction ability in the vertical direction along modal channels. Experimental results show that the proposed method surpasses the state-of-the-art comparative learning and MAE-based models in land cover classification tasks and reduces data input through vertical masking to achieve a more efficient model. Furthermore, additional experiments show that the proposed model has good generalization and can maintain good representation learning capabilities on small-scale data.

2）A remote sensing image self-supervised learning method based on SimMIM is pre-trained , and a MIM-SwinUNet is fine-tuned for land cover classification model supervisedly. The experiment shows that the self-supervised pre-trained model can effectively extract generalizable image features, and when transferred to downstream land cover classification tasks, it can achieve similar classification performance with significantly reduced labeled training sample size. Based on self-supervised labeling learning methods, multi-temporal remote sensing image land cover classification and land use change analysis are carried out in the case of Shanghai area using Sentinel 1 and 2 data.

3）The challenge is the exploration of these data and the timely delivery of focused information and knowledge in a simple understandable format. In this context we envisage the monitoring of Danube Delta and Black see costal areal. The study is directed to the modeling and understanding of climate change effects, particularly droughts and maritime currents. Droughts are studied using multispectral Satellite Image Time Series (SITS) of Sentinel 2. The study case is focused on the ensemble of lakes between the Black Sea coast and Danube Delta for the period 2019 to 2022. The Sentinel 2 SITS are analyzed to quantitively measure the lakes water surface, as the case of lake “Nuntasi”. During the 2020 drought the lake was completely dearth, a channel was built connecting it to the neighboring larger lake and refilling it. The SITS characterizes both the water level and quality variation. The Black Sea surface current of in the coastline limitrophe area are analyzed using SAR SITS from Sentinel 2. The maritime surface currents are characterized estimating the Doppler frequency of the SAR images. The SITS data are used to predict current patterns

As the most common form of ocean movement, mesoscale eddies promote the redistribution of marine variables, such as temperature, salinity and nutrients, through the transport of material and energy. They have an important influence on the marine biogeochemistry cycle, marine ecosystem and marine heat balance etc. Through the 2D/3D structural visualization of multiple variables of mesoscale eddies, the motion patterns of mesoscale eddies are directly visual through graphics and images, greatly contributing to studying mesoscale eddies.

Under the Euler coordinate, various methods for extracting mesoscale eddies have been proposed based on their basic features, among which the sea level anomaly (SLA)-based methods have performed better because these methods are able to avoid extra noise and excess eddy detections. A previous SLA-based method has been provided to identify and track global eddies. This highly effective orthogonal parallel algorithm greatly improves the efficiency of recognition without reducing the accuracy of mesoscale eddy recognition. The global eddy identification and trajectory dataset is built with a total time span between 1993 and 2020, which provides a data foundation for the subsequent study of mesoscale eddies. Affected by the modulation of various physical mechanisms and the complex marine environments, there are also complex dynamic processes such as eddy splitting, eddy merging, and dipoles. In this case, an automatic recognition method of global eddy dipoles is developed in terms of the mesoscale eddy dataset and the transmission modes as well with the characteristics of dipoles are simultaneously analyzed. In addition, an algorithm named EddyGraph for tracking mesoscale eddy splitting and merging events is come up with based on multi-level topological relationships, which helps to analyze the statistical characterization of global eddy splitting and merging events.

Under the Lagrangian coordinate, eddies are the cumulative results of the state of the fluid within a given time scale, which can maintain material coherence over the specified time intervals. By using the elliptic Lagrangian coherent structures, a typical black-hole eddy is extracted based on the data of the geostrophic flow velocity field. Combined with multi-source satellite remote sensing data and in-situ data, it shows that the black-hole eddy boundary can describe material transport more objectively than the Euler eddy boundary on a longer time scale. On the regional scale, Lagrangian eddies in the Western Pacific are successfully extracted and their spatial and temporal variations are analyzed. Through normalized chlorophyll data, it is observed that Lagrangian eddies can cause chlorophyll aggregation and hole effects. These findings demonstrate the important role of Lagrangian eddies in material transport. Nevertheless, the high calculation cost during the integration process has become a bottleneck, especially when the data resolution is improved or the study area is enlarged. Therefore, SLA-based orthogonal parallel detection of global rotationally coherent Lagrangian eddies is built, whose runtime is much faster than the previous nonparallel method. Finally, a dataset of long-term global Lagrangian eddies is established.

Based on objective reference framework and criteria, the extraction and visualization of the mesoscale eddy coreline, an ocean three-dimensional structure, are achieved by extracting the valley line of the obtained from objective flow field calculations as the eddy coreline. At the same time, equipped with an integrated visualization system, named i4Ocean, a standard morphological model of the transfer function for ocean thermohaline anomaly data and pressure anomaly data is designed from the number of feature points, feature color mapping and the line shape. Volume rendering technology and spherical ray casting algorithm are utilized to more clearly and completely display the large-scale ocean 3D eddies under the condition of ensuring the rendering quality. Based on 2D and 3D flow field vector data, the spatio-temporal continuity of ocean flow field visualization is enhanced under the whole spatio-temporal continuous framework of pathline-pathline. The geometry-based visualization animation of trace becomes smoother and more stable after solving the problem of aliasing in previous visualized ocean flow fields. Applying region-based eddy detection techniques (ow method, Q method, and Ω method) to ocean flow fields, the extracted mesoscale eddies are more comprehensive. Based on the ow criterion, Q criterion, and Ω criterion, standard transfer functions are constructed to optimize the extraction effect of ocean mesoscale eddies, reduce the difficulty of analyzing ocean mesoscale eddies through user interaction transfer functions, and improve the efficiency of user interaction analysis of ocean mesoscale eddies.

Agriculture is the largest water user worldwide and irrigation water management is facing important challenges in sustainable development of food production and water use. Improving irrigation water efficiency is a must in our changing world and requires extensive, comprehensive and accurate tools (physically based). Satellite data, as largely recognized, may play an important role in supporting data for agricultural models, especially to determine crop water needs or phenological crop status. While using satellite data to support agriculture may seem intuitive and straightforward, there is a strong need for accuracy in retrieving agricultural model parameters and state variables especially when the object is high resolution for precise agriculture, a key approach to food production and irrigation water management. In this respect the present DRAGON 5 project, thanks to ESA and the Ministry of Science and Technology (MOST), focuses on the exploitation of visible, thermal and microwave satellite data for operative agriculture.

The Chinese and Italian research groups since many years use satellite data for soil moisture assessment and precise agriculture modelling on several test sites in China and Italy, as well as in other places of the world, characterized by different crop cover and heterogeneity, different climates, irrigation practices. Indeed, satellite data together with field data and soil water balance models contribute to the accuracy needed in precision agriculture. In the past two years, the project work examined data from case studies in China, Italy, Africa and global scale.

In China, over agricultural fields in Shiyang River Basin (northwestern China) the present work supports the development of tools for crop type characterization, evapotranspiration estimation and irrigation water need: 1) Early-Season Crop Identification Using a Deep Learning Algorithm and Time-Series Sentinel-2 (S2) Data in Shiyang River Basin in China Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. 2) A data-driven high spatial resolution model to estimate biomass accumulation and crop yield using S2 and other satellite data was developed and applied in the Shiyang River Basin in northwestern China. For highly heterogeneous desert-oasis agroecosystem characterized by dominant crops, i.e., spring wheat, maize, sunflower, and melon, the developed model relies on three major innovations: i) the identification of start/end of the growing season of crops is done using NDVI from the S2 MSI (Multi-Spectral Instrument) in combination with limited local phenological information; ii) ETMonitor ET at 1km resolution was downscaled to 10m resolution to monitor crop water stress indicator in the biomass/yield model; iii) the air temperature stress indicator in the biomass/yield model was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system.

Taking the Sahel as an example, we investigated the impacts of land use/land cover change (LULC) and climate variability on the water balance components in 1990-2020 in three typical basins in the Sahel (Senegal, Niger rivers and Lake Chad) by using satellite-observation-based evapotranspiration derived from our model ETMonitor and ESA CCI soil moisture. The outcomes give useful hydrological insights into water and land management, emphasizing the crucial role of water recycling. This study has been published in Journal of Hydrology: Regional Studies and will be presented as a poster by a young scientist at the Dragon 5 symposium.

Soil moisture (SM) derived from microwave remote sensing is very useful, although the spatial resolution is not favorable for agricultural water use monitoring in farmland scale. The topography influences the emitted brightness temperature observed by a satellite microwave radiometer, leading to uncertainties in SM retrieval. A new methodology using the first brightness Stokes parameter observed by the Soil Moisture and Ocean Salinity (SMOS) was proposed to improve SM retrieval under complex topographic conditions. This work has been published in IEEE JSTARS and will be presented as a poster by a young scientist at the Dragon 5 symposium.

In Italy irrigated fields within the domain of irrigation consortia have been used as test area for SM and irrigation water demand estimates using satellite data and pixel-wise water-energy balance model (FEST-EWB) for different soil types and land cover heterogeneity.

Satellite data were used by FEST-EWB model: 1) for control model state variable (LST) and relative SM over large areas pixel-wise computed by the FEST-EWB model, solving the energy and water balances (Corbari-Mancini, 2014); ii) for definition of input parameter maps (e.g., leaf area Index, vegetational fraction cover).

The first approach analyses different scheme of soil water energy balance equations in consideration of remote sensing data crop or arboreal land cover heterogeneity comparing simulated energy, mass fluxes and relative surface temperature with fluxes observed at ground station and surface temperature from satellite. Using this approach a crop trees total evapotranspiration modelled with the water-energy balance scheme FEST-EWB seems to be slightly affected by the spatial resolution. For this reason, in the crop trees field the two-source modelling approach of the water and energy FEST-EWB seems to better explain the evapotranspiration from the vegetated pixel and soil components. Indeed, in the specific case study where LST are not different between trees and grass covering the interrow, similar values of latent heat are computed using both two-source and one-source energy water balance models.

Pixelwise land surface temperature computed by the hydrologic model have been compared with Satellite LST (Sentinel 3, Landsat 7, 8) showing the possibility to quantitative control pixel wise soil water balance model with the satellite data on large extension.

The second approach uses a coupled vegetation growth model with soil water and energy balance FEST-EWB-SAFY showing consistent estimates of LAI against satellite image information. This is also confirmed by modelled crop yields on the entire irrigation season respect to the observed yields for tomatoes and maize crop.

The project results obtained for the different case studies strengthen the idea that a synergic use of satellite data in water and energy balance models is a robust approach for irrigation engineer controlling crop water use of large irrigation district at high spatial resolution.

Predictions of agroecosystem processes as well as the hydrological and biogeochemical cycles in response to climate change and human interventions are needed both at continental level and at management-relevant scales. Obtaining such information is challenging since a multitude of essential variables need to be monitored to evaluate all relevant processes and to generate an agricultural digital twin. Natural hydrological and biogeochemical processes are additionally altered by anthropogenic drivers. The research community has to face this scientific challenge by a comprehensive consideration of multi-compartment interactions and scale-dependent relationships to enable the prediction of the response of agricultural systems to changing environmental conditions. Especially the current role of agriculture as a carbon source needs to be critically evaluated and strategies developed to transform farming systems into sinks for carbon.

To this end, in the fifth phase of the Dragon Cooperation (Dragon 5), we propose a project (No. 59197) to carry out agroecosystem health diagnosis and investigate agricultural processes based on various in situ and earth observation data, allowing to conserve, protect and improve the efficiency in the use of natural resources to facilitate sustainable agriculture development. At the mid-term of the Dragon 5, this paper summarizes individual steps of our project to gain knowledge about full agroecosystem states and processes by remote sensing, exemplarily for regions in Europe and China, in order to present our understanding of linking agroecosystem monitoring with carbon farming through multi-source remote sensing observations. The current study provides remote sensing approaches to identify crops such as object extraction based on SAR observations and individual plant detection by UAV; to monitor crop biophysical parameters such as leaf area index and biomass; to record hydrological states such as soil moisture, evapotranspiration, drought stress; as well as to finally provide a carbon budget (e.g., soil organic carbon content, gross primary productivity and net primary productivity) for agricultural fields. This can be seen as a workflow scheme of combining essential variables in the agricultural domain to meet the multiple challenges for providing a basis for mitigation measures, if they are at the continental level for policy advisory or at the local level to inform directly involved farmers to support sustainable agriculture development.

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. The lack of available global CO₂ and CH₄ measurements makes it difficult to estimate their emissions accurately. Satellite measurements would be very helpful for understanding the global CO₂ and CH₄ flux distribution if CO₂ and CH₄ column-averaged dry air mole fractions (XCO₂ and XCH₄) could be measured with a precision of better than 2 ppm. To this point, the main objectives of this research project in Dragon 5 is to use a combination of ground-based measurements of CO₂ and CH₄ and data from current satellite observations (TanSat, GOSAT/-2, OCO-2/-3 and TROPOMI) to validate and evaluate satellite retrievals with retrieval inter-comparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄. In this presentation, we will introduce our newly progress on CO₂ and CH₄ concentration measurement from in-flight and future satellite, and the CO₂ and CH₄ flux inversed from satellite and ground base measurement. The next generation of TanSat mission kicked-off in last two years, the new design of TanSat mission will provide wilder measurement in the swath to cover the global in daily observation. The preliminary OSSE on global and regional scale introduce the error reduction efficiency of TanSat-2 mission, and we also develop a new method to separate the ecosystem and anthropogenic emission which will be helpful for atmospheric inversion method toward the Global Stocktake. The TanSat mission has been used in city carbon emission signature investigation, which proof the TanSat capability on the anthropogenic emission signal Identify. We also developed UAV and ground-based CO2 measurement network, e.g. CHACOON for the carbon monitoring system build, and validations for satellite.

The Greenhouse Gas Monitoring Instrument (GMI) is a short-wavelength infrared (SWIR) hyperspectral-resolution spectrometer onboard the Chinese satellite GaoFen5-02 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The method about the on-board calibration and authenticity verification method are designed and the results are analyzed, and the results illustrate that the specifications meet the mission’s requirements. The on-board calibration results showed that the calibration coefficient range of the O2 channel is 1.05–1.15, the mean value is 1.10 and the standard deviation is 2.72%; the calibration coefficient of the CO2-1 channel is 1.05–1.13, the mean value is 1.09 and the standard deviation is 2.64%; the calibration coefficient of the CH4 channel is 1.08–1.10, the mean value is 1.11 and the standard deviation is 2.73%; the calibration coefficient of the CO2-2 channel is 1.09–1.14, the mean value is 1.12 and the standard deviation is 2.93%. The above results show that the radiation performance of each channel of the GMI shows no significant attenuation during this period, that the site calibration coefficient has no significant fluctuation and that the in-orbit operation state is stable. The authenticity verification results showed that the CO2 column concentration deviation of the satellite ground synchronization inversion was about 1.5 ppm, and the CH4 column concentration deviation was about 11.3 ppb, which verified the on-orbit detection accuracy of the GMI, and laid a foundation for the subsequent satellite inversion algorithm optimization and systematic error correction.

It is presented in this paper some recent progresses of ESA-MOST China Dragon Cooperation Program “Synergistic Monitoring of Ocean Dynamic Environment from Multi-Sensors (ID. 58009)” including: (1) Assessment of ocean swell height observations from Sentinel-1A/B Wave Mode against buoy in situ and modeling hindcasts; (2) Quantifying uncertainties in the partitioned swell heights observed from CFOSAT SWIM and Sentinel-1 SAR via triple collocation; (3) Up-to-Downwave asymmetry of the CFOSAT SWIM fluctuation spectrum for wave direction ambiguity removal; (4) Validation of wave spectral partitions from SWIM instrument on-board CFOSAT against in situ data; (5) Quality assessment of CFOSAT SCAT wind products using in situ measurements from buoys and research vessels; (6) Direct ocean surface velocity measurements from space in tropical cyclones; and (7) Deep learning-based model for reconstructing inner-core high winds in tropical cyclones using satellite remote sensing.

Polar lows are small and intense high latitude maritime cyclones and frequently induce typical ocean disasters such as strong winds, high waves and heavy rainfall. They remain difficult to observe and forecast due to their short lifetime (<48 hours) and small horizontal scales (200~1000 km). Satellite remote sensing is an important manner to monitor polar lows because of sparse synoptic observing network existing in subarctic and Arctic oceans. Previous studies subjectively identified polar lows by visual inspection of satellite thermal infrared imagery. However, this subjective visual analysis method is time-consuming and inevitably involves error in polar low detections. In this study, we present an automatic procedure to objectively detect and track a polar low occurring in Greenland Sea using spaceborne synthetic aperture radar (SAR) and passive microwave radiometer data. Based on the marker-controlled watershed segmentation method and the morphological image processing algorithm, Sentine-1A and RADARSAT-2 high-resolution SAR images and successive total atmospheric water vapor content field observations from multiple radiometers (e.g., AMSR2, SSM/I, GMI) are used to fix the center location of polar low. The track of this polar low is further determined from detected centers. The polar low detections are confirmed by the presence of cloud vortex signatures visible on the AVHRR and MODIS thermal infrared imagery, and the SAR-retrieved ocean surface high wind speeds. The results show that the proposed method has potential to efficiently detect and track polar low from multi-sensor data.

In 2022, this project continues to carry out research on space radiation benchmark transfer and calibration technology based on the RadCalNet site according to the plan. The accuracy of on-orbit satellite radiation calibration was improved, and the transfer calibration method based on RadCalNet TOA reflectivity products was improved by using a high accuracy and stability reference satellite. The TOA reflectance conversion model of Baotou site was optimized. The TOA reflectance model of the American site and Namibian site was constructed. Based on the above, an uncertain transfer link was constructed that connects a spatial radiation reference to each of the RadCalNet ground sites. This link can provide measurements of the contributions to total uncertainty caused by each factor.

Surface reflectance is the fundamental quantity required in the majority of optical Earth Observation analyses, and as an essential input to derive biophysical products. In addition to parameters such as the fraction of vegetation cover (FCOVER) and Canopy Chlorophyll Content (CCC), these products also include essential climate variables (ECVs) such as leaf area index (LAI). LAI is an integral plant canopy attribute and critical indicator of plant growth status. Currently several satellite derived LAI products exist, covering local to global scales with various spatial resolutions. In turn, they are crucial in understanding vegetation productivity/yield, biogeochemical cycles, and the weather and climate systems. Therefore, validation of such products is of great importance to ensure they meet the accuracy requirements for specific applications. However, ground measurements are not always match reflective of the spatial resolution of the satellite imagery, and contribute to uncertainty in the validation of LAI products. The key to reducing this source of uncertainty is upscaling from ground-measured LAI values to data representative of a satellite pixel.

In the study, high-spatial-resolution UAV remote sensing images were used as an intermediary for upscaling processing. We applied this approach to validate LAI retrievals based on Sentinel-2 and Gaofen-6 imagery (in which the Sentinel-2 Level-2 Prototype Processor (SL2P) was used to retrieve LAI from Sentinel-2, whilst a look-up-table (LUT) method was used to retrieve LAI from Gaofen-6). UAV images can well connect ground data and satellite data, thereby reducing the error caused by the mismatch of spatial resolution.

In the mid-term of the project, this study collects field LAI data and UAV images in Taizishan Forest Park (30.91-30.92°N, 112.87-112.88°E), China. Very high spatial resolution LAI reference maps were derived from the UAV imagery using four vegetation indices (VIs). In order to verify the LAI products retrieved by Sentinel-2 and Gaofen-6, we upscaled the UAV LAI map to 10m and 16m resolution. Finally, we compared the UAV-based upscaling approach to the direct comparison between LAI retrievals and ground measurements. Our results revealed improved correspondence between the satellite retrievals and UAV-based reference map when compared to direct comparison with the ground measurements (RMSE reduced from 1.02 to 0.59 for Sentinel-2 and 1.49 to 0.89 for Gaofen-6). Compared to SL2P, larger MAE(≥0.59) and RMSE(≥0.76) values were obtained for the Gaofen-6 LAI retrievals, indicating a need for further algorithm refinement.

Abstract

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. Using satellite remote sensing data to monitor the production state of shipyard dynamically has the advantages of high efficiency and objectivity. Further, dock, shipway, assembly area, material storage area and other shipbuilding places are the indispensable part of shipbuilding industry, which can reflect the production activity of the shipyard. This study performs object detection for docks based on high-resolution remote sensing images and deep learning methods. Meanwhile, according to the imaging characteristics of optical remote sensing images and SAR images of shipbuilding places, we used satellite remote sensing data to dynamically monitor the shipyard production state from spatial and time series perspective.

The study firstly uses an object detection network based on the deformable spatial attention module (DSAM), which can be used to detect the docks on high spatial resolution remote sensing image. This network solve the object detection problems caused by the limit of actual docks and the diversity of docks. Secondly, since the backbone of the dock object detection network is with the excellent feature extraction capability for docks, this study connects the backbone with a lightweight status recognition network (Status Head) to determine the dock production status information based on the features extracted from the backbone. Thirdly, we analyzed the backscattered features of shipbuilding places on SAR satellite images, and proposed a method to monitor shipyard production state by using multi-time SAR data. This method can reduce the error caused by insufficient time resolution when using high resolution optical remote sensing data to monitor the production state. Finally, the proposed method can accurately recognize the shipyard production state through experimental verification, which reflects the potential of satellite remote sensing images in shipyard production state monitoring, and also provides a new research thought perspective for other coastal industrial production state monitoring.

.

The work conducted in these three years of activity aims to make a quantitative use of remote sensing information in agriculture and to develop products targeted at optimizing the production and allowing a more sustainable agriculture (e.g. optimization in the use of fertilizers and pesticides). The project has been concentrated on the following themes: retrieval of biophysical variables of vegetation, estimation of bare soil properties, prediction of crop yield and monitoring pest and diseases. The project included three sites, Maccarese farm in Central Italy, some site in Central East-Africa, a site in the middle East and farms in the Quzhou district in China city of Handan, in the south of Hebei Province.

For vegetation physical and chemical parameters, taking advantage of the PRISMA and ENMAP EO data, the work is aiming to use the potential oh hyperspectral data in deriving biophysical parameter related to equivalent water thickness (EWT). In particular, we aim to retrieve EWT by using a constrain minimization procedure applying the Beer-Lambert law in the 940-1100nm range to derive the optically leaf active water layer in cm. The EWT retrieved with the minimization procedure are compared with the one retrieved by using the hybrid approach (i.e. radiative simulation and Machine Learning Regression). Test site are in central Italy, and in the far East.

For topsoil characterization, the purpose of this activity was to investigate the suitability of PRISMA and Sentinel-2 images for the retrieval of topsoil properties such as Soil Organic Matter, and nutrients like Nitrogen, Phosphorus, Potassium and pH in croplands. Procedure is based on different Machine Learning (ML) algorithms and spectra pre-treatment. Results in the study area located in the north-eastern China near the prefecture-level city of Handan, in the south of Hebei Province, revealed better accuracies in retrieving topsoil properties obtained by PRISMA data instead to the Sentinel-2 data.

For crop yield prediction, we generated a 30-m Chinese winter wheat yield dataset (ChinaWheatYield30m) for major winter wheat-producing provinces in China for the period 2016–2021 with a semi-mechanistic model (hierarchical linear model, HLM). The yield prediction model was built by considering the wheat growth status and climatic factors. It can estimate wheat yield with excellent accuracy and low cost using a combination of satellite observations and regional meteorological information (i.e., Landsat 8, Sentinel-2 and ERA5 data from the Google Earth Engine (GEE) platform). The results were validated by using in situ measurements and census statistics and indicated a stable performance of the HLM model based on calibration datasets across China, with r of 0.81** and nRMSE of 12.59 %. With regards to validation, the ChinaWheatYield30m dataset was highly consistent with in situ measurement data and census data, indicated by r (nRMSE) of 0.72** (15.34 %) and 0.73** (19.41 %). With its high spatial resolution and accuracy, the ChinaWheatYield30m is a valuable dataset that can support numerous applications, including crop production modeling and regional climate evaluation.

For what concerns crop threats, the core of the system aiming at detecting yellow rust outbreaks in maize and wheat crops, will be built on PRISMA satellite. Several VIs (NDVI, SIPI, PRI, PSRI, MSR) computed by using hyperspectral images will be used to implement a Diseases Infection Index. The DI will be classified into four classes including healthy (DI≤5%), slight infection (5<DI≤20%), moderate infection (20<DI≤50%), and severe infection (DI>50%). It should be underlined that the algorithms proposed by Guo et al. (2021) have been developed based on hyperspectral images acquired by drones. The impact of spatial resolution on the capability to detect yellow rust in crops will be one of the results of the activity.

In addition, we have established the remote sensing-based risk assessment methods for agricultural pests, specifically for grasshopper and desert locust. For grasshopper, we took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a remote sensing monitoring model for grasshopper potential habitat. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. We also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. For desert locust presence risk forecasting, we have proposed a dynamic prediction method of desert locust presence risk at Somalia-Ethiopia-Kenya. Monthly prediction experiments from February to December 2020 were conducted, extracting high, medium and low risk areas of desert locust occurrence in the study area. Results demonstrated that the overall accuracy was 77.46%, and the model enables daily dynamic forecasting of desert locust risk up to 16 days in advance, providing early warning and decision support for preventive ground control measures for the desert locust.

In summary, as of the third year of the Dragon 5, the project's execution progress is consistent with the schedule, and most of the activities have achieved good results. Additionally, some scholars in the project team are conducting scientific research using the data obtained through the cooperation between the two sides.

.